Autoencoder (AE) and Variational autoencoder (VAE) algorithms make use of latent variables to help with the prediction.
Since the whole image can be predicted with only one network inference, an auto-encoder algorithm such as L3C [1]} can decompress thousands of times faster than PixelCNN++ [2]}, but with a much worse compression ratio.
After bits-back [3]} scheme is introduced to VAE algorithm [4]}, the compression ratio is improved significantly.
Thus later works such as BB-ANS [4]}, Bit-Swap [6]} and HiLLoC [7]} all apply this scheme.
Algorithms such as NVAE [8]} and VDVAE [9]} can even get ratios comparable to auto-regressive ones.
However, bits-back introduces a dependency between different images, making it impossible to decompress a single one.
Furthermore, the cost of random initial bits can only be leveraged by compressing/decompressing a large number of images sequentially, affecting the batch size and transfer speed greatly.

